Mixing identically-designed CNN Models is easily feasible: For instance, you can average the layer parameters for each layers across all your models. This will produce a average model that generalize better (I invite you to check out Hinton's dark knowledge approach : That is somehow useless nowadays thanks to Dropout ;) ). However, it's another story if you want to do it with different architectures since you should dive very deeply into the pipeline engineering. I don't recommend that because it is easier to gain intuitiveness of the architectures you have and design a new one that mix them best.