Modular robots are made up of a set of components which can be configured and reconfigured to form customized robots for a wide range of tasks. Fully utilizing the flexibility of modular robots is challenging, as it requires the identification of optimal modular designs for each given task, often with limited computation and time. Previous works in design automation achieve efficient run-times by utilizing machine learning to create a one-to-one mapping from task to design. However, the problem of robot design is often multimodal, where multiple distinct designs can be similarly or equally good for a task. Alternative design solutions may be needed in the field, for instance, if a module in the optimal design fails and no replacement is available. This paper presents a novel method based on generative adversarial networks (GANs) that learns a one-to-many mapping from task to a distribution of designs. We apply our method to construct locomoting modular robots for terrains with varying obstacle heights and infill. We compare our method against the state-of-the-art, and find that our algorithm results in better solution quality, diversity, and alternatives for when the optimal design fails.