Convolutional Neural Network (CNN) models for modern applications are becoming increasingly deep and complex. Thus, the number of different CNN mapping possibilities when deploying a CNN model on multiple edge devices is vast. Design Space Exploration (DSE) methods are therefore essential to find a set of optimal CNN mappings subject to one or more design requirements. In this paper, we present an efficient DSE methodology to find (near-)optimal CNN mappings for distributed inference at the edge. To deal with the vast design space of different CNN mappings, we accelerate the searching process by proposing and utilizing a multi-stage hierarchical DSE approach together with a tailored Genetic Algorithm as the underlying search engine.