Open WiFi access points (APs) are demonstrating that they can provide opportunistic data services to moving vehicles. We present CrowdWiFi, a novel system to look up roadside WiFi APs located outdoors or inside buildings. CrowdWiFi consists of two components: online compressive sensing (CS) and offline crowdsourcing. Online CS presents an efficient framework for the coarse-grained estimation of nearby APs along the driving route, where received signal strength (RSS) values are recorded at runtime, and the number and location of the APs are recovered immediately based on limited RSS readings and adaptive CS operations. Offline crowdsourcing assigns the online CS tasks to crowd-vehicles and aggregates answers on a bipartite graphical model. Crowd-server also iteratively infers the reliability of each crowd-vehicle from the aggregated sensing results, and then refines the estimation of the APs using weighted centroid processing. Extensive simulation results and real testbed experiments confirm that CrowdWiFi can successfully reduce the computation cost and energy consumption of roadside WiFi lookup, while maintaining satisfactory localization accuracy.