Limited payload capacity on small unmanned aerial vehicles (UAVs) results in restricted flight time. In order to increase the operational range of UAVs, recent research has focused on the use of mobile ground charging stations. The cooperative route planning for both aerial and ground vehicles (GVs) is strongly coupled due to fuel constraints of the UAV, terrain constraints of the GV and the speed differential of the two vehicles. This problem is, in general, an NP-hard combinatorial optimization problem. Existing polynomial-time solution approaches make a trade-off in solution quality for large-scale scenarios and generate solutions with large relative gaps (up to 50 %) from known lower bounds. In this work, we employ a hybrid metaheuristic known as Construct, Merge, Solve & Adapt (CMSA) in order to develop a scalable and computationally efficient solution approach. We discuss results for large scale scenarios and provide a comparative analysis with the current state-of-the-art.