For quick interactive viewing, another option would be to send your plot into plotly so you can scan over for outliers:
library(plotly)
plot <- ggplot(data=females_black,aes(x = age, y = ht, label = id_comb)+
geom_line()
ggplotly(plot)
For this many individuals, it makes sense to explore subsets. One approach you might consider would be to classify your individuals on some criteria (e.g. max ht, best/worst fit to a linear model, fastest growth, age range in data set, k-means groups, etc.). Then you could plot those trajectories of those groups against a background of the full data set. That can be a helpful approach for finding underlying structure across many individuals.