Many wireless sensor network protocols are employing gossip-based message dissemination, where nodes probabilistically forward messages, to reduce message overhead. We are concerned with emerging systems in stationary sensor networks that are multiple-source with each message targeted at every recipient, such as query and code propagation. Default gossip-based approaches tend to treat each stream of messages from different senders independently of the others, overloading each node with message overhead summed from all streams. We apply intelligent scheduling strategies for gossip forwarding, effectively piggybacking streams atop one another, to address this significant message overhead. Our problem formulation introduces a new concept called the "semblance graph" used to schedule gossiping based on streams' gossip periods. Two new heuristic algorithms are proposed to solve the semblance graph problem. The performance of these two heuristics is on average within 3.5% of the optimal solution. Simulations show that the piggybacking strategy reduces the message, bandwidth, and energy-overhead while still maintaining the original scalability, reliability and latency of the canonical gossip.