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Last-mile delivery in the logistics chain contributes to emissions and increased congestion. Crowd-shipping is a sustainable and low-cost alternative to traditional delivery, but relies heavily on the availability of occasional couriers. In this work, we propose a hub-based crowd-shipping system that aims to attract sufficient potential crowd-shippers to serve a large portion of the demand for small parcels. While small-scale versions of this problem have been recently addressed, a scaling to larger instances significantly complexifies the problem. A heuristic approach based on continuum approximation is designed to evaluate the quality of a potential set of hub locations. By combining an efficient and accurate approximation method with a large neighborhood search heuristic, we are able to efficiently find a good set of hub locations, even for large scale networks. Furthermore, on top of determining good hub locations, our methods allow to identify the expected number of delivered parcels in every region, which can be used to design a smart dynamic assignment strategy. A case study on the Washington DC network shows that hubs are built at locations that are both geographically central, but most importantly are popular origins for crowd-shippers. The optimal number of hubs is mainly dependent on the marginal number of parcels that can be served by crowd-shippers from a specific hub, relative to the costs involved in opening that hub. The performance of our algorithm is close to that of a simulation-optimization algorithm, yet being up to 25 times faster. Thereby, the results show a dynamic assignment policy based on continuum approximation estimates outperforms existing assignment strategies.